Search Results - (((((((want OR wants) OR semantic) OR wien) OR cantor) OR anne) OR blaney) OR hints) algorithms.

Pediatric incontinence : evaluation and clinical management

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Networks-on-chip : from implementations to programming paradigms by Ma, Sheng, Huang, Libo, 1976-, Lai, Mingche, Shi, Wei

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Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. by Julian, David

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The next economic disaster : why it's coming and how to avoid it by Vague, Richard

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Digitalization of Society and Socio-Political Issues. 1, Digital, Communication, and Culture

Table of Contents: “…The Digitalization of Cultural Policies in France 149; Anne BELLON 14.1.…”
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Millimeter-wave digitally intensive frequency generation in CMOS by Wu, Wanghua, Staszewski, Robert Bogdan, 1965-, Long, John R. (John Robert), 1958-

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Integration of swarm intelligence and artificial neural network

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Statistical learning from a regression perspective by Berk, Richard A.

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Machine Learning in Chemical Safety and Health : Fundamentals with Applications. by Wang, Qingsheng

Table of Contents: “…Chapter 3 Flammability Characteristics Prediction Using QSPR Modeling -- 3.1 Introduction -- 3.1.1 Flammability Characteristics -- 3.1.2 QSPR Application -- 3.1.2.1 Concept of QSPR -- 3.1.2.2 Trends and Characteristics of QSPR -- 3.2 Flowchart for Flammability Characteristics Prediction -- 3.2.1 Dataset Preparation -- 3.2.2 Structure Input and Molecular Simulation -- 3.2.3 Calculation of Molecular Descriptors -- 3.2.4 Preliminary Screening of Molecular Descriptors -- 3.2.5 Descriptor Selection and Modeling -- 3.2.6 Model Validation -- 3.2.6.1 Model Fitting Ability Evaluation -- 3.2.6.2 Model Stability Analysis -- 3.2.6.3 Model Predictivity Evaluation -- 3.2.7 Model Mechanism Explanation -- 3.2.8 Summary of QSPR Process -- 3.3 QSPR Review for Flammability Characteristics -- 3.3.1 Flammability Limits -- 3.3.1.1 LFLT and LFL -- 3.3.1.2 UFLT and UFL -- 3.3.2 Flash Point -- 3.3.3 Auto-ignition Temperature -- 3.3.4 Heat of Combustion -- 3.3.5 Minimum Ignition Energy -- 3.3.6 Gas-liquid Critical Temperature -- 3.3.7 Other Properties -- 3.4 Limitations -- 3.5 Conclusions and Future Prospects -- References -- Chapter 4 Consequence Prediction Using Quantitative Property-Consequence Relationship Models -- 4.1 Introduction -- 4.2 Conventional Consequence Prediction Methods -- 4.2.1 Empirical Method -- 4.2.2 Computational Fluid Dynamics (CFD) Method -- 4.2.3 Integral Method -- 4.3 Machine Learning and Deep Learning-Based Consequence Prediction Models -- 4.4 Quantitative Property-Consequence Relationship Models -- 4.4.1 Consequence Database -- 4.4.2 Property Descriptors -- 4.4.3 Machine Learning and Deep Learning Algorithms -- 4.5 Challenges and Future Directions -- References -- Chapter 5 Machine Learning in Process Safety and Asset Integrity Management -- 5.1 Opportunities and Threats -- 5.2 State-of-the-Art Reviews -- 5.2.1 Artificial Neural Networks (ANNs).…”
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Theoretical Computer Science : Proceedings of the 10th Italian Conference on ICTCS '07. by Italiano, Giuseppe F.

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Listed Volatility and Variance Derivatives : a Python-based Guide. by Hilpisch, Yves

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XML for DB2 information integration

Table of Contents: “…-- 3.4 Creating an XML schema from a database schema -- 3.4.1 The algorithm.…”
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Design optimization of fluid machinery : applying computational fluid dynamics and numerical optimization by Kim, Kwang-Yong, 1956-, Samad, Abdus, Benini, Ernesto

Table of Contents: “…2.2.5.3 Periodic/Cyclic Boundary Conditions2.2.5.4 Symmetry Boundary Conditions; 2.2.6 Moving Reference Frame (MRF); 2.2.7 Verification and Validation; 2.2.8 Commercial CFD Software; 2.2.9 Open Source Codes; 2.2.9.1 OpenFOAM; References; Chapter 3 Optimization Methodology; 3.1 Introduction; 3.1.1 Engineering Optimization Definition; 3.1.2 Design Space; 3.1.3 Design Variables and Objectives; 3.1.4 Optimization Procedure; 3.1.5 Search Algorithm; 3.2 Multi-Objective Optimization (MOO); 3.2.1 Weighted Sum Approach; 3.2.2 Pareto-Optimal Front…”
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From complexity in the natural sciences to complexity in operation management systems by Briffaut, Jean-Pierre

Table of Contents: “…Complexity in perspective -- 1.2.1. Etymology and semantics -- 1.2.2. Methods proposed for dealing with complexity from the Middle Ages to the 17th Century and their current outfalls -- 1.3. …”
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Anesthesia student survival guide : a case-based approach

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Fog and fogonomics : challenges and practices of fog computing, communication, networking, strategy, and economics

Table of Contents: “…5.5.2 Survivability, Availability, and Reliability 122 -- 5.6 Sovereignty, Privacy, Security, Interoperability, and Management 123 -- 5.6.1 Data Sovereignty 123 -- 5.6.2 Privacy and Security 123 -- 5.6.3 Heterogeneity and Interoperability 124 -- 5.6.4 Monitoring, Orchestration, and Management 124 -- 5.7 Trade-Offs 125 -- 5.8 Conclusion 126 -- References 126 -- 6 Incentive Schemes for User-Provided Fog Infrastructure 129 /George Iosifidis, Lin Gao, Jianwei Huang, and Leandros Tassiulas -- 6.1 Introduction 129 -- 6.2 Technology and Economic Issues in UPIs 132 -- 6.2.1 Overview of UPI models for Network Connectivity 132 -- 6.2.2 Technical Challenges of Resource Allocation 134 -- 6.2.3 Incentive Issues 135 -- 6.3 Incentive Mechanisms for Autonomous Mobile UPIs 137 -- 6.4 Incentive Mechanisms for Provider-assisted Mobile UPIs 140 -- 6.5 Incentive Mechanisms for Large-Scale Systems 143 -- 6.6 Open Challenges in Mobile UPI Incentive Mechanisms 145 -- 6.6.1 Autonomous Mobile UPIs 145 -- 6.6.1.1 Consensus of the Service Provider 145 -- 6.6.1.2 Dynamic Setting 146 -- 6.6.2 Provider-assisted Mobile UPIs 146 -- 6.6.2.1 Modeling the Users 146 -- 6.6.2.2 Incomplete Market Information 147 -- 6.7 Conclusions 147 -- References 148 -- 7 Fog-Based Service Enablement Architecture 151 /Nanxi Chen, Siobhán Clarke, and Shu Chen -- 7.1 Introduction 151 -- 7.1.1 Objectives and Challenges 152 -- 7.2 Ongoing Effort on FogSEA 153 -- 7.2.1 FogSEA Service Description 156 -- 7.2.2 Semantic Data Dependency Overlay Network 158 -- 7.2.2.1 Creation and Maintenance 159 -- 7.2.2.2 Semantic-Based Service Matchmarking 161 -- 7.3 Early Results 164 -- 7.3.1 Service Composition 165 -- 7.3.1.1 SeDDON Creation in FogSEA 167 -- 7.3.2 Related Work 168 -- 7.3.2.1 Semantic-Based Service Overlays 169 -- 7.3.2.2 Goal-Driven Planning 170 -- 7.3.2.3 Service Discovery 171 -- 7.3.3 Open Issue and Future Work 172 -- References 174 -- 8 Software-Defined Fog Orchestration for IoT Services 179 /Renyu Yang, Zhenyu Wen, David McKee, Tao Lin, Jie Xu, and Peter Garraghan.…”
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Fuzzy Multiple Attribute Decision Making : Methods and Applications by Chen, Shu-Jen

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Cybersecurity Law, standards and regulations by Schreider, Tari

Table of Contents: “…Authors of original encryption algorithms never really thought that governments would want to have access to their en... -- In an effort to bring sanity to the uncontrolled growth of encryption regulations, two important laws have been introduced. …”
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Handbook of safety principles

Table of Contents: “…Success or Failure / Ann Enander -- 30.8. Relations to Other Safety Principles / Ann Enander -- References / Ann Enander -- Further Reading / Ann Enander -- 31. …”
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XIVth International Congress on Mathematical Physics : Lisbon, 28 July - 2 August 2003

Table of Contents: “…Lieb (Princeton), Jakob Yngvason (U. Wien). Semiclassical dynamics of an electron moving in a slowly perturbed periodic potential / Stefan Teufel, Gianluca Panati (Tech. …”
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